Using machine learning to diagnose bacterial sepsis in the critically ill patients

Yang Liu, Kup Sze Choi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early antibiotic therapy to patients with sepsis is necessary. Every hour of therapy delay could reduce the survival chance of patients with severe sepsis by 7.6%. Certain biomarkers like blood routine and C-reactive protein (CRP) are not sufficient to diagnose bacterial sepsis, and their sensitivity and specificity are relatively low. Procalcitonin (PCT) is the best diagnostic biomarker for sepsis so far, but is still not effective when sepsis occurs with some complications. Machine learning techniques were thus proposed to support diagnosis in this paper. A backpropagation artificial neural network (ANN) classifier, a support vector machine (SVM) classifier and a random forest (RF) classifier were trained and tested using the electronic health record (EHR) data of 185 critically ill patients. The area under curve (AUC), accuracy, sensitivity, and specificity of the ANN, SVM, and RF classifiers were (0.931, 90.8%, 90.2%, 91.6%), (0.940, 88.6%, 92.2%, 84.3%) and (0.953, 89.2%, 88.2%, 90.4%) respectively, which outperformed PCT where the corresponding values were (0.896, 0.716, 0.952, 0.822). In conclusion, the ANN and SVM classifiers explored have better diagnostic value on bacterial sepsis than any single biomarkers involve in this study.
Original languageEnglish
Title of host publicationSmart Health - International Conference, ICSH 2017, Proceedings
PublisherSpringer Verlag
Pages223-233
Number of pages11
ISBN (Print)9783319679631
DOIs
Publication statusPublished - 1 Jan 2017
EventInternational Conference on Smart Health, ICSH 2017 - Hong Kong, Hong Kong
Duration: 26 Jun 201727 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10347 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Smart Health, ICSH 2017
CountryHong Kong
CityHong Kong
Period26/06/1727/06/17

Keywords

  • Artificial neural network
  • Bacterial sepsis
  • Diagnostic value
  • Machine learning
  • Sepsis
  • Support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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